Calculating the Dynamic Parameter of the Maseng-Bakken Model for Earth-LEO Links
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Bibliographic record
Abstract
Stochastic dynamic models of rain attenuation play a critical role in the development of fade mitigation techniques for wireless systems operating at frequencies above 10 GHz. The widely used Maseng-Bakken model is based on the observation that rain attenuation is lognormally distributed and can be modelled as a one-dimensional stationary Gauss-Markov (also known as an Ornstein-Uhlenbeck process). A key aspect of the model is the use of a single dynamic parameter, <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\beta$</tex>, to capture the time variation of rain fades. The model has been used to model rain fading on both terrestrial fixed links and Earth-space links to geostationary satellites, and a form of the model has been recommended by ITU-R. Previous attempts to extend the model to Earth-space links to LEO satellites have been reported, but, without explanation, have used the same value of the dynamic parameter as used in the geostationary case even though the cause of the time variation is quite different. Here, we show how the dynamic parameter can be determined for the case of links from ground stations to LEO satellites by simulating rain fading based on realistic two-dimensional maps of rain cells generated using synthetic storm techniques, estimating the fade slope distribution, and then adjusting <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\beta$</tex> in the Maseng-Bakken model to generate fading with a matching distribution. This approach has allowed us to reveal the power law relationship between <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\beta$</tex> and satellite altitude, and thereby correct a major limitation of previous efforts.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
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Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it